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<div class="title">cvfh.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *  Copyright (c) 2012-, Open Perception, Inc.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
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<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
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<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
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<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#ifndef PCL_FEATURES_IMPL_CVFH_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#define PCL_FEATURES_IMPL_CVFH_H_</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/features/cvfh.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/features/normal_3d.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;pcl/features/pfh_tools.h&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="centroid_8h.html">pcl/common/centroid.h</a>&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00051"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_v_f_h_estimation.html#af3263e05fc67057d005373ac2ad30ba5">   51</a></span>&#160;<a class="code" href="classpcl_1_1_c_v_f_h_estimation.html#af3263e05fc67057d005373ac2ad30ba5">pcl::CVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::compute</a> (<a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;{</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_feature.html">Feature&lt;PointInT, PointOutT&gt;::initCompute</a> ())</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  {</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  }</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="comment">// Resize the output dataset</span></div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <span class="comment">// Important! We should only allocate precisely how many elements we will need, otherwise</span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;  <span class="comment">// we risk at pre-allocating too much memory which could lead to bad_alloc </span></div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="comment">// (see http://dev.pointclouds.org/issues/657)</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (1);</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160; </div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;  <span class="comment">// Perform the actual feature computation</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  computeFeature (output);</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160; </div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <a class="code" href="classpcl_1_1_feature.html">Feature&lt;PointInT, PointOutT&gt;::deinitCompute</a> ();</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;}</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160; </div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_v_f_h_estimation.html#a07e011d11c58ff21b8dc99bff3416571">   74</a></span>&#160;<a class="code" href="classpcl_1_1_c_v_f_h_estimation.html#a07e011d11c58ff21b8dc99bff3416571">pcl::CVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::extractEuclideanClustersSmooth</a> (</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> &amp;cloud,</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> &amp;normals,</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <span class="keywordtype">float</span> tolerance,</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="keyword">const</span> pcl::search::Search&lt;pcl::PointNormal&gt;::Ptr &amp;tree,</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    std::vector&lt;pcl::PointIndices&gt; &amp;clusters,</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="keywordtype">double</span> eps_angle,</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster,</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster)</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;{</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  <span class="keywordflow">if</span> (tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">getInputCloud</a> ()-&gt;points.size () != cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  {</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n&quot;</span>, tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">getInputCloud</a> ()-&gt;points.size (), cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  }</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () != normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  {</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n&quot;</span>, cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  }</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  std::vector&lt;bool&gt; processed (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160; </div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="comment">// Process all points in the indices vector</span></div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ()); ++i)</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;  {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    <span class="keywordflow">if</span> (processed[i])</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160; </div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    std::vector&lt;unsigned int&gt; seed_queue;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="keywordtype">int</span> sq_idx = 0;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    seed_queue.push_back (i);</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160; </div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    processed[i] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="keywordflow">while</span> (sq_idx &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (seed_queue.size ()))</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <span class="comment">// Search for sq_idx</span></div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      <span class="keywordflow">if</span> (!tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">radiusSearch</a> (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;      {</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;      }</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160; </div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 1; j &lt; nn_indices.size (); ++j) <span class="comment">// nn_indices[0] should be sq_idx</span></div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      {</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        <span class="keywordflow">if</span> (processed[nn_indices[j]]) <span class="comment">// Has this point been processed before ?</span></div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160; </div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        <span class="comment">//processed[nn_indices[j]] = true;</span></div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;        <span class="comment">// [-1;1]</span></div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <span class="keywordtype">double</span> dot_p = normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[0] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[0]</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                     + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[1] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[1]</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;                     + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[2] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[2];</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160; </div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="keywordflow">if</span> (fabs (acos (dot_p)) &lt; eps_angle)</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        {</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;          processed[nn_indices[j]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;          seed_queue.push_back (nn_indices[j]);</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        }</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;      }</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;      sq_idx++;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    }</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="comment">// If this queue is satisfactory, add to the clusters</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    <span class="keywordflow">if</span> (seed_queue.size () &gt;= min_pts_per_cluster &amp;&amp; seed_queue.size () &lt;= max_pts_per_cluster)</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    {</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> r;</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      r.indices.resize (seed_queue.size ());</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; seed_queue.size (); ++j)</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        r.indices[j] = seed_queue[j];</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160; </div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      std::sort (r.indices.begin (), r.indices.end ());</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160; </div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;      r.header = cloud.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a>;</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;      clusters.push_back (r); <span class="comment">// We could avoid a copy by working directly in the vector</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    }</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;  }</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;}</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160; </div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00162"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_v_f_h_estimation.html#adcb190964bcb8ee94db3bae2e9ed79a4">  162</a></span>&#160;<a class="code" href="classpcl_1_1_c_v_f_h_estimation.html#adcb190964bcb8ee94db3bae2e9ed79a4">pcl::CVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::filterNormalsWithHighCurvature</a> (</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointNT&gt;</a> &amp; cloud,</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    std::vector&lt;int&gt; &amp;indices_to_use,</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    std::vector&lt;int&gt; &amp;indices_out,</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    std::vector&lt;int&gt; &amp;indices_in,</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <span class="keywordtype">float</span> threshold)</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;{</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  indices_out.resize (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  indices_in.resize (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160; </div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  <span class="keywordtype">size_t</span> in, out;</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  in = out = 0;</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160; </div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (indices_to_use.size ()); i++)</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  {</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices_to_use[i]].curvature &gt; threshold)</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    {</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;      indices_out[out] = indices_to_use[i];</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;      out++;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    }</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    {</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      indices_in[in] = indices_to_use[i];</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      in++;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;    }</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  }</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160; </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  indices_out.resize (out);</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  indices_in.resize (in);</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;}</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00195"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_v_f_h_estimation.html#a253fa1afb8a592e7af807ddd99ec779e">  195</a></span>&#160;<a class="code" href="classpcl_1_1_c_v_f_h_estimation.html#a253fa1afb8a592e7af807ddd99ec779e">pcl::CVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::computeFeature</a> (<a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;{</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  <span class="comment">// Check if input was set</span></div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="keywordflow">if</span> (!normals_)</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  {</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::computeFeature] No input dataset containing normals was given!\n&quot;</span>, getClassName ().c_str ());</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  }</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  <span class="keywordflow">if</span> (normals_-&gt;points.size () != surface_-&gt;points.size ())</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::computeFeature] The number of points in the input dataset differs from the number of points in the dataset containing the normals!\n&quot;</span>, getClassName ().c_str ());</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  }</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160; </div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;  centroids_dominant_orientations_.clear ();</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160; </div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  <span class="comment">// ---[ Step 0: remove normals with high curvature</span></div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;  std::vector&lt;int&gt; indices_out;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  std::vector&lt;int&gt; indices_in;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  filterNormalsWithHighCurvature (*normals_, *indices_, indices_out, indices_in, curv_threshold_);</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160; </div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  pcl::PointCloud&lt;pcl::PointNormal&gt;::Ptr normals_filtered_cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> ());</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (indices_in.size ());</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; indices_in.size (); ++i)</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  {</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x = surface_-&gt;points[indices_in[i]].x;</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y = surface_-&gt;points[indices_in[i]].y;</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z = surface_-&gt;points[indices_in[i]].z;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  }</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;  std::vector&lt;pcl::PointIndices&gt; clusters;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160; </div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;  <span class="keywordflow">if</span>(normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size() &gt;= min_points_)</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;  {</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <span class="comment">//recompute normals and use them for clustering</span></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    KdTreePtr normals_tree_filtered (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointNormal&gt;</a> (<span class="keyword">false</span>));</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    normals_tree_filtered-&gt;setInputCloud (normals_filtered_cloud);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160; </div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    <a class="code" href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation&lt;PointNormal, PointNormal&gt;</a> n3d;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    n3d.<a class="code" href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">setRadiusSearch</a> (radius_normals_);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    n3d.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (normals_tree_filtered);</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    n3d.<a class="code" href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">setInputCloud</a> (normals_filtered_cloud);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    n3d.<a class="code" href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">compute</a> (*normals_filtered_cloud);</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160; </div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    KdTreePtr normals_tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointNormal&gt;</a> (<span class="keyword">false</span>));</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    normals_tree-&gt;setInputCloud (normals_filtered_cloud);</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160; </div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    extractEuclideanClustersSmooth (*normals_filtered_cloud,</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;                                    *normals_filtered_cloud,</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;                                    cluster_tolerance_,</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                                    normals_tree,</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                                    clusters,</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;                                    eps_angle_threshold_,</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;                                    <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (min_points_));</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160; </div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;  }</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160; </div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;  <a class="code" href="classpcl_1_1_v_f_h_estimation.html">VFHEstimator</a> vfh;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;  vfh.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (surface_);</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;  vfh.<a class="code" href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">setInputNormals</a> (normals_);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;  vfh.<a class="code" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a>(indices_);</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;  vfh.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (this-&gt;tree_);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">setUseGivenNormal</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#afe8d57987178f9ac5b9a220dbca2e02f">setUseGivenCentroid</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#ac2b2d9368996748a9ea3b5250976ad01">setNormalizeBins</a> (normalize_bins_);</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a507f4095fcf5b7bb1e153f79632e35d7">setNormalizeDistance</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a1569fb923259faeabcc41243b2266b30">setFillSizeComponent</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160; </div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;  <span class="comment">// ---[ Step 1b : check if any dominant cluster was found</span></div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  <span class="keywordflow">if</span> (clusters.size () &gt; 0)</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  { <span class="comment">// ---[ Step 1b.1 : If yes, compute CVFH using the cluster information</span></div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160; </div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; clusters.size (); ++i) <span class="comment">//for each cluster</span></div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;      Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;      Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; clusters[i].indices.size (); j++)</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;      {</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        avg_normal += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getNormalVector4fMap ();</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        avg_centroid += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getVector4fMap ();</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;      }</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160; </div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      avg_normal /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      avg_centroid /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160; </div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;      Eigen::Vector4f centroid_test;</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;      <a class="code" href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a> (*normals_filtered_cloud, centroid_test);</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;      avg_normal.normalize ();</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160; </div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;      Eigen::Vector3f avg_norm (avg_normal[0], avg_normal[1], avg_normal[2]);</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;      Eigen::Vector3f avg_dominant_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160; </div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;      <span class="comment">//append normal and centroid for the clusters</span></div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;      dominant_normals_.push_back (avg_norm);</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      centroids_dominant_orientations_.push_back (avg_dominant_centroid);</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    }</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160; </div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <span class="comment">//compute modified VFH for all dominant clusters and add them to the list!</span></div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (dominant_normals_.size ());</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (dominant_normals_.size ());</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160; </div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; dominant_normals_.size (); ++i)</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    {</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      <span class="comment">//configure VFH computation for CVFH</span></div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8bb453b6e0fbae76da240b9205123e1b">setNormalToUse</a> (dominant_normals_[i]);</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">setCentroidToUse</a> (centroids_dominant_orientations_[i]);</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;      <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::VFHSignature308&gt;</a> vfh_signature;</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">compute</a> (vfh_signature);</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;      output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i] = vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0];</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    }</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;  }</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;  { <span class="comment">// ---[ Step 1b.1 : If no, compute CVFH using all the object points</span></div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    Eigen::Vector4f avg_centroid;</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <a class="code" href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a> (*surface_, avg_centroid);</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    Eigen::Vector3f cloud_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    centroids_dominant_orientations_.push_back (cloud_centroid);</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160; </div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <span class="comment">//configure VFH computation for CVFH using all object points</span></div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">setCentroidToUse</a> (cloud_centroid);</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">setUseGivenNormal</a> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160; </div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::VFHSignature308&gt;</a> vfh_signature;</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">compute</a> (vfh_signature);</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160; </div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (1);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = 1;</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0] = vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0];</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;  }</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;}</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_CVFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::CVFHEstimation&lt;T,NT,OutT&gt;;</span></div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;<span class="preprocessor">#endif    </span><span class="comment">// PCL_FEATURES_IMPL_VFH_H_ </span></div>
<div class="ttc" id="acentroid_8h_html"><div class="ttname"><a href="centroid_8h.html">centroid.h</a></div></div>
<div class="ttc" id="aclasspcl_1_1_c_v_f_h_estimation_html_a07e011d11c58ff21b8dc99bff3416571"><div class="ttname"><a href="classpcl_1_1_c_v_f_h_estimation.html#a07e011d11c58ff21b8dc99bff3416571">pcl::CVFHEstimation::extractEuclideanClustersSmooth</a></div><div class="ttdeci">void extractEuclideanClustersSmooth(const pcl::PointCloud&lt; pcl::PointNormal &gt; &amp;cloud, const pcl::PointCloud&lt; pcl::PointNormal &gt; &amp;normals, float tolerance, const pcl::search::Search&lt; pcl::PointNormal &gt;::Ptr &amp;tree, std::vector&lt; pcl::PointIndices &gt; &amp;clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits&lt; int &gt;::max)())</div><div class="ttdoc">Region growing method using Euclidean distances and neighbors normals to add points to a region.</div><div class="ttdef"><b>Definition:</b> cvfh.hpp:74</div></div>
<div class="ttc" id="aclasspcl_1_1_c_v_f_h_estimation_html_a253fa1afb8a592e7af807ddd99ec779e"><div class="ttname"><a href="classpcl_1_1_c_v_f_h_estimation.html#a253fa1afb8a592e7af807ddd99ec779e">pcl::CVFHEstimation::computeFeature</a></div><div class="ttdeci">void computeFeature(PointCloudOut &amp;output)</div><div class="ttdoc">Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at a set of points given by &lt;s...</div><div class="ttdef"><b>Definition:</b> cvfh.hpp:195</div></div>
<div class="ttc" id="aclasspcl_1_1_c_v_f_h_estimation_html_adcb190964bcb8ee94db3bae2e9ed79a4"><div class="ttname"><a href="classpcl_1_1_c_v_f_h_estimation.html#adcb190964bcb8ee94db3bae2e9ed79a4">pcl::CVFHEstimation::filterNormalsWithHighCurvature</a></div><div class="ttdeci">void filterNormalsWithHighCurvature(const pcl::PointCloud&lt; PointNT &gt; &amp;cloud, std::vector&lt; int &gt; &amp;indices_to_use, std::vector&lt; int &gt; &amp;indices_out, std::vector&lt; int &gt; &amp;indices_in, float threshold)</div><div class="ttdoc">Removes normals with high curvature caused by real edges or noisy data</div><div class="ttdef"><b>Definition:</b> cvfh.hpp:162</div></div>
<div class="ttc" id="aclasspcl_1_1_c_v_f_h_estimation_html_af3263e05fc67057d005373ac2ad30ba5"><div class="ttname"><a href="classpcl_1_1_c_v_f_h_estimation.html#af3263e05fc67057d005373ac2ad30ba5">pcl::CVFHEstimation::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Overloaded computed method from pcl::Feature.</div><div class="ttdef"><b>Definition:</b> cvfh.hpp:51</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_from_normals_html_a349685ac9deb723502de9f399d0286dc"><div class="ttname"><a href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">pcl::FeatureFromNormals::setInputNormals</a></div><div class="ttdeci">void setInputNormals(const PointCloudNConstPtr &amp;normals)</div><div class="ttdoc">Provide a pointer to the input dataset that contains the point normals of the XYZ dataset....</div><div class="ttdef"><b>Definition:</b> feature.h:344</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html"><div class="ttname"><a href="classpcl_1_1_feature.html">pcl::Feature</a></div><div class="ttdoc">Feature represents the base feature class. Some generic 3D operations that are applicable to all feat...</div><div class="ttdef"><b>Definition:</b> feature.h:106</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_a44829319486a2dc415a4e068dc55c577"><div class="ttname"><a href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">pcl::Feature::setRadiusSearch</a></div><div class="ttdeci">void setRadiusSearch(double radius)</div><div class="ttdoc">Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...</div><div class="ttdef"><b>Definition:</b> feature.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ace1caca622f06eee8ad1911228324792"><div class="ttname"><a href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">pcl::Feature::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> feature.h:166</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ad5b1fa9612da40e738b1d99252c5ff2f"><div class="ttname"><a href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">pcl::Feature::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Base method for feature estimation for all points given in &lt;setInputCloud (), setIndices ()&gt; using th...</div><div class="ttdef"><b>Definition:</b> feature.hpp:189</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation</a></div><div class="ttdoc">NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point....</div><div class="ttdef"><b>Definition:</b> normal_3d.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html_ac92dbbea9d923754b3d87d981a6bd131"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">pcl::NormalEstimation::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> normal_3d.h:290</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_ab219359de6eb34c9d51e2e976dd1a0d1"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">pcl::PCLBase::setIndices</a></div><div class="ttdeci">virtual void setIndices(const IndicesPtr &amp;indices)</div><div class="ttdoc">Provide a pointer to the vector of indices that represents the input data.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:73</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html">pcl::VFHEstimation</a></div><div class="ttdoc">VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...</div><div class="ttdef"><b>Definition:</b> vfh.h:72</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a1569fb923259faeabcc41243b2266b30"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a1569fb923259faeabcc41243b2266b30">pcl::VFHEstimation::setFillSizeComponent</a></div><div class="ttdeci">void setFillSizeComponent(bool fill_size)</div><div class="ttdoc">set size_component_</div><div class="ttdef"><b>Definition:</b> vfh.h:203</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a507f4095fcf5b7bb1e153f79632e35d7"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a507f4095fcf5b7bb1e153f79632e35d7">pcl::VFHEstimation::setNormalizeDistance</a></div><div class="ttdeci">void setNormalizeDistance(bool normalize)</div><div class="ttdoc">set normalize_distances_</div><div class="ttdef"><b>Definition:</b> vfh.h:193</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a65c5f646bfbe38de7ea99e86f118a1c1"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">pcl::VFHEstimation::setUseGivenNormal</a></div><div class="ttdeci">void setUseGivenNormal(bool use)</div><div class="ttdoc">Set use_given_normal_</div><div class="ttdef"><b>Definition:</b> vfh.h:145</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a7fceb91de436785e2f00ef1184416956"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">pcl::VFHEstimation::setCentroidToUse</a></div><div class="ttdeci">void setCentroidToUse(const Eigen::Vector3f &amp;centroid)</div><div class="ttdoc">Set centroid_to_use_</div><div class="ttdef"><b>Definition:</b> vfh.h:174</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a8ad7f79ee618d1a6f9bca5d579c36130"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">pcl::VFHEstimation::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Overloaded computed method from pcl::Feature.</div><div class="ttdef"><b>Definition:</b> vfh.hpp:65</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a8bb453b6e0fbae76da240b9205123e1b"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a8bb453b6e0fbae76da240b9205123e1b">pcl::VFHEstimation::setNormalToUse</a></div><div class="ttdeci">void setNormalToUse(const Eigen::Vector3f &amp;normal)</div><div class="ttdoc">Set the normal to use</div><div class="ttdef"><b>Definition:</b> vfh.h:155</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_ac2b2d9368996748a9ea3b5250976ad01"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#ac2b2d9368996748a9ea3b5250976ad01">pcl::VFHEstimation::setNormalizeBins</a></div><div class="ttdeci">void setNormalizeBins(bool normalize)</div><div class="ttdoc">set normalize_bins_</div><div class="ttdef"><b>Definition:</b> vfh.h:183</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_afe8d57987178f9ac5b9a220dbca2e02f"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#afe8d57987178f9ac5b9a220dbca2e02f">pcl::VFHEstimation::setUseGivenCentroid</a></div><div class="ttdeci">void setUseGivenCentroid(bool use)</div><div class="ttdoc">Set use_given_centroid_</div><div class="ttdef"><b>Definition:</b> vfh.h:164</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_a441f41e648d284d68e1f2015d40f5e7c"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">pcl::search::Search::radiusSearch</a></div><div class="ttdeci">virtual int radiusSearch(const PointT &amp;point, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const =0</div><div class="ttdoc">Search for all the nearest neighbors of the query point in a given radius.</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_ac4a83e895b2a11e89319673117a927fa"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">pcl::search::Search::getInputCloud</a></div><div class="ttdeci">virtual PointCloudConstPtr getInputCloud() const</div><div class="ttdoc">Get a pointer to the input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> search.h:125</div></div>
<div class="ttc" id="agroup__common_html_gaf5729fae15603888b49743b118025290"><div class="ttname"><a href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a></div><div class="ttdeci">unsigned int compute3DCentroid(ConstCloudIterator&lt; PointT &gt; &amp;cloud_iterator, Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid)</div><div class="ttdoc">Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.</div><div class="ttdef"><b>Definition:</b> centroid.hpp:50</div></div>
<div class="ttc" id="astructpcl_1_1_point_indices_html"><div class="ttname"><a href="structpcl_1_1_point_indices.html">pcl::PointIndices</a></div><div class="ttdef"><b>Definition:</b> PointIndices.h:13</div></div>
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